Scroll down to read more about major global incidents of violence against aid workers from January 1997 - Febraury 2025.

Let’s start by looking at the total number of incidents.

An incident is an event or attack of major violence against aid workers. Major violence includes acts with the following outcomes: kidnapping, killing, wounding (i.e. serious injury requiring medical attention), rape and sexual assault.

“Aid workers are defined as the employees and associated personnel (paid and volunteers) of not-forprofit aid agencies (both national and international) that provide material and technical assistance in humanitarian relief contexts.” - Aid Worker Security Database Codebook February 2025

From 1997 to the end of February 2025, there were 4,337 attacks against aid workers.

Let’s break this up by year.

There were some peaks over the years, mostly in 2008, 2013, 2020, and 2024. Overall, we see an upward trajectory in the number of incidents over time, with 2024 having 938.2% more incidents than in 1997. The drop seen in 2025 is due to data only available for the first two months of the year.

One act of major violence could result in more than one aid worker being affected though. Let’s see how the number of attacks compare to total number of people affected for each year.

Not only are the number of attacks increasing, but the amount of aid workers harmed during each attack is increasing. For example, in 2024, there were 362 more aid workers affected than the number of incidents.

Let’s deep dive into 2024, where there is both the largest number of incidents and most aid workers affected per incident.

These are the top 5 countries with most number of attacks 2024 and the types of attacks that make of these incidents.

Here we can see how the top attack types from the five countries with the most incidents in 2024 — Occupied Palestinian Territories, South Sudan, Sudan, Nigeria, and Lebanon — resulted in different types of harm to aid workers.

Aerial Bombardment, Shelling, and Shooting are the deadliest type of attacks (aside from attack types that were recorded as “unknown”). What are the motives driving these attacks?

Still looking at the same 5 countries, motives behind attacks that cost the most lives include: Disputed, Economic, Incidental, Political, and Unknown.

import {Inputs} from "@observablehq/inputs"
import {interval} from "@mootari/range-slider"
// Double-sided year filter (range slider)
year_range_def = interval([1997, 2025], {
    value: [1997, 2025],
    step: 1,
    label: "Select Years"
  });

select_motive_def = Inputs.select(motive_types, {value: motive_types, multiple: true, label: "Motive"});

select_attack_def = Inputs.select(attack_types, {value: attack_types, multiple: 4, label: "Attack Type"});


inc_map = {
  const map = L.map("map").setView([20, 0], 2);
  L.tileLayer("https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png", {
    attribution: '© OpenStreetMap contributors'
  }).addTo(map);
  return map
}

A Note on Unknown or Ambiguous Data

There is a large amount of “Unknown” data seen in the attacks (11.8% to be exact) and ambiguity for the motives. These gaps reflect the challenging environments in which aid workers operate, where the immediate priority is saving lives and responding to crises, not comprehensive data collection. While these limitations reduce analytic precision, they also highlight the need for systems that support rapid response and post-incident reporting without placing additional burden on those in the field.

Below is a map of all the incidents. You can filter by year, motive of attack, and type of attack. To select more than one motive and attack type, hold shift while selecting.

html`
  <link
    rel="stylesheet"
    href="https://unpkg.com/leaflet.markercluster@1.5.3/dist/MarkerCluster.css"
  />
  <link
    rel="stylesheet"
    href="https://unpkg.com/leaflet.markercluster@1.5.3/dist/MarkerCluster.Default.css"
  />`

html`<div id="map" style="height: 500px; width: 1000px"></div>`

html`
  <div style="display: flex; gap: 2rem; position: absolute">
    <div id="slider-container"></div>
    <div id="checkbox-container"></div>
    <div id="attack-container"></div>
  </div>
`

render = {
  document.getElementById("slider-container").append(year_range_def)
  document.getElementById("checkbox-container").append(select_motive_def)
  document.getElementById("attack-container").append(select_attack_def)
}

viewof year_range = year_range_def
viewof select_motive = select_motive_def
viewof select_attack = select_attack_def


// Wait until DOM element is created
viewof map =  {
    // Load MarkerCluster plugin CSS & JS
  await require("https://unpkg.com/leaflet.markercluster@1.5.3/dist/leaflet.markercluster.js")
  
  // Remove existing layers before re-adding
  inc_map.eachLayer(layer => {
    if (layer instanceof L.MarkerClusterGroup || layer instanceof L.Marker) {
      inc_map.removeLayer(layer)
    }
  });

  // Filter points by Year
  const points = JSON.parse(incident_points)
  let filteredPoints = points.filter(d => {
      const y = +d.year
      return y >= year_range[0] && y <= year_range[1]
    })
  if (select_motive) {
    filteredPoints = filteredPoints.filter(d => {
      const motive = d.motive
      return select_motive.includes(motive)
    })
  }
  if (select_attack) {
    filteredPoints = filteredPoints.filter(d => {
      const attack = d.attack
      return select_attack.includes(attack)
    })
  }
  const useClustering = true;
  if (useClustering) {
    const markers = L.markerClusterGroup()

    filteredPoints.forEach(d => {
      if (d.lat && d.lon) {
        const marker = L.marker([+d.lat, +d.lon])
          .bindPopup(d.popup_text)
        markers.addLayer(marker)
      }
    });
    inc_map.addLayer(markers)
  } else {
    filteredPoints.forEach(d => {
      if (d.lat && d.lon) {
        L.marker([+d.lat, +d.lon]).bindPopup(d.popup_text).addTo(inc_map)
      }
    });
  }
  return inc_map;
};

Conclusions and Recommendations

The amount of attacks on aid workers has steady increased since 1997 and each attack is resulting in more people being kidnapped, wounded or killed. In 2024, there were a record number of recorded incidents and affected aid workers. In the 5 countries with the highest number of incidents, aerial bombarment was the most common attack, and also most deadliest attack. Disputed, incidental, and unknown are the main motives recorded for aerial bombardment.

Knowing what the most common type of attack is and the motivations behind it can help aid agencies inform, prepare, and equip aid workers to minimize risk in the field. Agencies operating in high-risk countries should prioritize training protocols and protective measures specifically for aerial bombardments, including shelter access, early-warning systems, and real-time threat monitoring. Policymakers must advocate for stricter enforcement of international humanitarian law and support conflict resolution initiatives that target the root causes of violence. Although there are limitations in the data, the lives of those delivering humanitarian aid can be better protected by knowing where and how aid workers are most at risk, and understanding the recent patterns of violence — even when motives are unclear — and using that knowledge to inform field preparedness, advocacy, and response protocols.

References

All defintions referenced from the Aid Worker Security Database Codebook February 2025

ChatGPT used for code debugging and map functionality

Scrollytelly inspiration from Beyond the Modifiable Areal Unit Problem!, by François Sémécurbe and EURO 2024 Final Scrollytelling Analysis – by Óscar Bartolomé Pato